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Snowflake for Developers/Guides/Parallelized Time Series Analysis of Restaurant Foot Traffic
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Parallelized Time Series Analysis of Restaurant Foot Traffic

Hex Staff
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Overview

This solution architecture shows how to use Snowpark User-Defined Table Functions to forecast the foot traffic of a restaurant chain by locations. 

  • Run pre-processing and feature engineering using Snowpark
  • Use Snowpark UDTF to train several forecasting models in parallel for different store locations

Solution Architecture: Time series forecasting with Snowpark UDTF and Hex

Architecture Diagram
  • In this use-case, you learn how to use Snowpark to analyze the store locations and customer traffic data.
  • The solution shows how to use Snowpark UDTFs to train several ML models in parallel.

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Updated Apr 28, 2026

This content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake instances